The amount of data in our world has been exploding. All this data need to be stored and analyzed to extract value. The fundamental requirements for data storage and analysis to meet the rapid growth in data rates include:                1. Capacity—Seamlessly store and analyze peta-bytes of data;        2. Scalability—Add more compute and storage capacities as data storage requirements grow;        3. Accessibility—Maintain continuous access to stored data in the presence of hardware failures;        4. Performance—Increase performance as more resources are added incrementally; and        5. Cost—Maintain low total cost of ownership.        
However, conventional data storage architectures do not provide an efficient solution that addresses all of these requirements without any trade-offs. Additionally, current data storage architectures cannot provide access storage in a shared environment with a minimum of protocol overhead.